Employing RNN and Petri Nets to Secure Edge Computing Threats in Smart Cities

Abstract

The Industrial Internet of Things (IIoT) revolution has led to the development a potential system that enhances communication among a city's assets. This system relies on wireless connections to numerous limited gadgets deployed throughout the urban landscape. However, technology has exposed these networks to various harmful assaults, cyberattacks, and potential hacker threats, jeopardizing the security of wireless information transmission. Specifically, unprotected IIoT networks act as vulnerable backdoor entry points for potential attacks. To address these challenges, this project proposes a comprehensive security structure that combines Extreme Learning Machines based Replicator Neural Networks (ELM-RNN) with Deep Reinforcement Learning based Deep Q-Networks (DRL-DQN) to safeguard against edge computing risks in intelligent cities. The proposed system starts by introducing a distributed authorization mechanism that employs an established trust paradigm to effectively regulate data flows within the network. Furthermore, a novel framework called Secure Trust-Aware Philosopher Privacy and Authentication (STAPPA), modeled using Petri Net, mitigates network privacy breaches and enhances data protection. The system employs the Garson algorithm alongside the ELM-based RNN to optimize network performance and strengthen anomaly detection capabilities. This enables efficient determination of the shortest routes, accurate anomaly detection, and effective search optimization within the network environment. Through extensive simulation, the proposed security framework demonstrates remarkable detection and accuracy rates by leveraging the power of reinforcement learning.